Image reconstruction system and method in magnetic resonance imaging

ABSTRACT

A method and system for image reconstruction are provided. Multiple coil images may be obtained. A first reconstructed image based on the multiple coil images may be reconstructed based on a first reconstruction algorithm. A second reconstructed image based on the multiple coil images may be reconstructed based on a second reconstruction algorithm. Correction information about the first reconstructed image may be generated based on the first reconstructed image and the second reconstructed image. A third reconstructed image may be generated based on the first reconstructed image and the correction information about the first reconstructed image.

TECHNICAL FIELD

The present disclosure generally relates to magnetic resonance imaging(MRI), and more particularly, to an image reconstruction system andmethod in MRI.

BACKGROUND

MRI is a widely used medical technique. However, reconstructed images inMRI may include intensity inhomogeneity, which may ultimately causemisdiagnose. Thus, it may be desirable to develop an imagereconstruction method and system that may remove or reduce intensityinhomogeneity to improve the quality of reconstructed image.

SUMMARY

The present disclosure relates to MRI. One aspect of the presentdisclosure relates to a method for image reconstruction. The method mayinclude one or more of the following operations. Multiple coil imagesmay be obtained. A first reconstructed image based on the multiple coilimages may be reconstructed according to a first reconstructionalgorithm. A second reconstructed image based on the multiple coilimages may be reconstructed according to a second reconstructionalgorithm. Correction information about the first reconstructed imagemay be generated based on the first reconstructed image and the secondreconstructed image. A third reconstructed image may be generated basedon the first reconstructed image and the correction information aboutthe first reconstructed image.

In some embodiments, the first reconstruction algorithm may be a sum ofsquares algorithm.

In some embodiments, the second reconstruction algorithm may be ageometric average algorithm.

In some embodiments, the reconstructing a first reconstructed image orthe reconstructing the second reconstructed image may include one ormore of the following operations. For each point of a plurality ofpoints in the imaged object, pixel coordinates of corresponding pixelsin the multiple coil images relating to the point of the imaged objectmay be determined. Pixel values of the corresponding pixels in themultiple coil images of the point may be obtained. The firstreconstructed image or the second reconstructed image may bereconstructed based on the pixel coordinates and the pixel values of thecorresponding pixels in the multiple coil images of the plurality ofpoints in the imaged object.

In some embodiments, the correction information may relate to intensityinhomogeneity of the first reconstructed image.

In some embodiments, the generating correction information relating tothe intensity inhomogeneity of the first reconstructed image may furtherinclude dividing the first reconstructed image by the secondreconstructed image to generate a divided image.

In some embodiments, the generating a third reconstructed image mayfurther include dividing the first reconstructed image by the dividedimage.

In some embodiments, the generating correction information relating tothe intensity inhomogeneity further include one or more of the followingoperations. The divided image may be smoothed to generate a smootheddivided image. The smoothed divided image may be normalized to generatea normalized image.

In some embodiments, the generating a third reconstructed image mayfurther include dividing the first reconstructed image by the normalizedimage.

A further aspect of the present disclosure relates to a system for imagereconstruction. The system may include a coil image generation module, areconstruction module and a correction module. The coil image generationmodule may be configured to generate multiple coil images. Thereconstruction module may be configured to generate a firstreconstructed image based on the multiple coil images based on a firstreconstruction algorithm and generate a second reconstructed image basedon the multiple coil images based on a second reconstruction algorithm.The correction module may be configured to generate a thirdreconstructed image by correcting the first reconstructed image based onthe second reconstructed image.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 illustrates a schematic diagram of an imaging system 100according to some embodiments of the present disclosure;

FIG. 2 illustrates an architecture of a computer on which a specializedsystem incorporating the present teaching may be implemented;

FIG. 3A illustrates an exemplary image processing device according tosome embodiments of the present disclosure;

FIG. 3B illustrates an exemplary reconstruction module according to someembodiments of the present disclosure;

FIG. 3C illustrates an exemplary correction module according to someembodiments of the present disclosure;

FIG. 4 illustrates a flowchart illustrating an exemplary process forimage reconstruction in accordance with some embodiments of the presentdisclosure;

FIG. 5 is a flowchart illustrating an exemplary process for imagereconstruction in accordance with some embodiments of the presentdisclosure;

FIG. 6 is a flowchart illustrating an exemplary process for correcting areconstructed image in accordance with some embodiments of the presentdisclosure;

FIG. 7 is a flowchart illustrating an exemplary process for obtainingcorrection information in accordance with some embodiments of thepresent disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for generatingcorrection information in accordance with some embodiments of thepresent disclosure;

FIG. 9 is a flowchart illustrating an exemplary process for imagereconstruction in accordance with some embodiments of the presentdisclosure;

FIG. 10 illustrates a reconstructed liver image based on a GA algorithm;

FIG. 11 illustrates a reconstructed liver image based on an SOSalgorithm;

FIG. 12 illustrates a divided image of a reconstructed liver image basedon an SOS algorithm and a reconstructed liver image based on a GAalgorithm; and

FIG. 13 illustrates a corrected image of a reconstructed liver imagebased on an SOS algorithm.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by otherexpression if they may achieve the same purpose.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to” another unit,engine, module, or block, it may be directly on, connected or coupledto, or communicate with the other unit, engine, module, or block, or anintervening unit, engine, module, or block may be present, unless thecontext clearly indicates otherwise. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include” and/or“comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof.

The present disclosure provided herein relates to relates to magneticresonance imaging (MRI). Specially, the present disclosure relates to animage reconstruction system and method in MRI. According to someembodiments of the present disclosure, the method may include obtainingmultiple coil images and generating a first reconstructed image and asecond reconstructed image based on the multiple coil images using tworeconstruction algorithms. The method may further including generatingcorrection information about the first reconstructed image based on thefirst and the second reconstructed image and correcting the firstreconstructed image based on the correction information.

FIG. 1 illustrates a schematic diagram of an imaging system 100according to some embodiments of the present disclosure. Imaging system100 may include a magnetic resonance imaging (MRI) device 110, an imageprocessing device 120, a terminal 130, a display 140, a database 150,and a network 160. In some embodiments, at least part of imageprocessing device 120 may be implemented on computer 200 shown in FIG.2.

MRI device 110 may obtain MR image data. The MR image data may includespatial encoding information about an imaged object. The MR image datamay also be referred to as K space data. The MR image data may betransferred to imaging processing device 120. Imaging processing device120 may process the MR data to generate an MR image. In someembodiments, the MR image data may include one or more MR signals.

MRI device 110 may include an MRI scanner, a main magnet, a gradientmagnet system, and a radiation frequency (RF) system (not shown in FIG.1), or the like, or a combination thereof. The MRI scanner may beconfigured to place an imaged object. The MRI scanner may be a tunneltype MRI scanner 150 (i.e., a close-bore MRI scanner etc.), or an openMRI scanner (i.e., an open-bore MRI scanner etc.).

A main magnet of MRI device 110 may generate a static magnetic fieldduring a process of imaging. The main magnet may be of various typesincluding, a permanent magnet, a superconducting magnet, anelectromagnet, or the like, or a combination thereof. The main magnetmay have any magnetic field intensity, for example, 0.35 T, 0.5 T, 1 T,1.5 T, 3 T, etc. Merely by way of example, the magnetic field intensityof the main magnet may be 1.5 T.

A gradient magnet system of MRI device 110 may generate magnet fieldgradients to a main magnet field in one or more directions. For example,the gradient magnet system may generate field gradients to the mainmagnet field in x, y, and z directions. The gradient magnet system mayinclude a plurality of gradient coils in different directions.

An RF system of MRI device 110 may include a RF transmitter coil and aRF receiver coil. The RF transmitter coil and/or the RF receiver coilmay be a birdcage coil, a transverse electromagnetic coil, a surfacecoil, a saddle coil, a solenoid coil, a saddle coil, a flexible coil, orthe like, or a combination thereof. The RF transmitter coil may transmitRF field towards the imaged object to generate magnetic resonancephenomenon. The RF transmitter coil may transmit RF pulse with any echotime (TE) and repetition time (TR). The TE of RF pulse may be anypositive number, for example, 1 millisecond, 2 milliseconds, 30milliseconds, 100 milliseconds, or the like, or a combination thereof.Merely by way of example, the TE of RF pulse may be 2.2 milliseconds.The TR pulse may be any positive number, for example, 1 millisecond, 2milliseconds, 30 milliseconds, 100 milliseconds, or the like, or acombination thereof. In some embodiments, the TR of RF pulse may be 4.9ms.

An RF receiver coil of MRI device 110 may receive and/or amplify MRsignal. In some embodiments, MRI device 110 may include multiple RFreceiver coils. The multiple RF receiver coils may have various spatialsensitivity and may receive MR signals in parallel.

In some embodiments, MRI device 110 may include an analog-to-digitalconverter (ADC) (not shown in FIG. 1). The analog-to-digital convertermay convert MR signals received by one or more RF receiver coils into MRimage data. The analog-to-digital converter may be a direct-conversionADC, a successive-approximation ADC, a ramp-compare ADC, a WilkinsonADC, an integrating ADC, a delta-encoded ADC, a pipeline ADC, asigma-delta ADC, or the like, or a combination thereof.

Image processing device 120 may generate and/or process an MR image. TheMR image may be a coil image, a reconstructed MR image, adiffusion-weighted image, a diffusion tensor image, a perfusion-weightedimage, a functional MR image, a sensitivity weighted image, an MRspectroscopy image, or the like, or a combination thereof. The MR imagemay be a four-dimensional (4D) image, a three-dimensional (3D) image, atwo-dimensional (2D) image, or the like, or a combination thereof. TheMR image may be an image of any object (e.g., a brain, a breast, aheart, an anocelia, an abdominal, etc.).

An MR image may have any pixel bandwidth (BW). For example, the BW of anMR image may be 20 Hz/pixel, 100 Hz/pixel, 300 Hz/pixel, or the like.Merely by way of example, the BW of an MR image may be 345 Hz/pixel. TheMR image may have any field-of-view (FOV). For example, the FOV of an MRimage may be 40*40 mm, 256*256 mm, 192*256 mm, or the like. In someembodiments, the FOV of an MR image may be 260*260 mm. The MR image mayhave any resolution. For example, the resolution of MR image may be256*256, 1024*1024, 2048*2048, or the like. In some embodiments, theresolution of an MR image may be 256*256.

In some embodiments, an MR image may be a coil image. Image processingdevice 120 may generate a coil image based on MR image data. The MRimage data may also be referred to as K space data. The MR image datamay include spatial encoding information about an imaged object. The MRimage data may be obtained by MRI device 110 or retrieved from anothersource (e.g., database 150, a storage, etc.). Merely by way of example,image processing device 120 may generate the coil image based on MRimage data using a Fourier transform algorithm. In some embodiments, MRIdevice 110 may include multiple RF receiver coils. Image processingdevice 120 may generate multiple coil images corresponding to each RFreceiver coil.

Image processing device 120 may generate a reconstructed MR image basedon MR image data or multiple coil images. The MR image data may beobtained by MRI device 110 or retrieved from another source (e.g.,database 150, a storage, etc.). The multiple coil images may begenerated by image processing device 120 or retrieved from anothersource (e.g., database 150, a storage, etc.). More descriptions aboutthe generation of a reconstructed image based on multiple coil imagesmay be found elsewhere in the present disclosure. See, for example, FIG.5 and the description thereof.

Imaging processing device 120 may process an MR image. Exemplaryprocessing may include enhancing an image to generate an enhanced image,extracting some information from an image, correcting intensityinhomogeneity of an image, or the like, or a combination thereof. Imageprocessing may include performing one or more operations on the image.Exemplary operations may include image manipulation (e.g., rotating,flipping, resizing, cropping, etc.), image correction, image weighting,image subtraction, image division, image segmentation, imagebinarization, image overlapping, image matching, image negative filmdevelopment, image noise reduction, image enhancement, imagecompression, or the like, or a combination thereof. Exemplary imagecorrection may include intensity inhomogeneity correction, imagedistortion correction, gradient nonlinearity correction, motion artifactcorrection, color correction, or the like, or a combination thereof.

Image processing device 120 may be any kind of device that may processan image. For example, image processing device 120 may include ahigh-performance computer specialized in image processing or transactionprocessing, a personal computer, a portable device, a server, amicroprocessor, an integrated chip, a digital signal processor (DSP), apad, a PDA, or the like, or a combination thereof. In some embodiments,imaging processing device 120 may be implemented on computer 200 shownin FIG. 2.

Image processing may involve an image reconstruction technique, anintensity inhomogeneity correction technique, or the like, or acombination thereof. As used herein, “correcting” intensityinhomogeneity may refer to completely or partially remove intensityinhomogeneity that is present or identified by an image processingtechnique.

Image reconstruction technique applicable to MR image data may include asimultaneous acquisition of spatial harmonic (SMASH) algorithm, anAUTO-SMASH algorithm, a variable density AUTO-SMASH algorithm, ageneralized auto-calibrating partially parallel acquisition (GRAPPA)algorithm, a generalized-SMASH algorithm, sensitivity profiles from anarray of coils for encoding and reconstruction in a parallel (SPACE RIP)algorithm, or the like, or a combination thereof. The imagereconstruction technique based on multiple coil images may include a sumof squares (SOS) algorithm, a geometric average (GA) algorithm, asensitivity encoding (SENSE) algorithm, a parallel imaging withlocalized sensitivities (PILS) algorithm, a modified sensitivityencoding (MSENSE) algorithm, a SPACE RIP algorithm, or the like, or acombination thereof. Merely by way of example, image processing device120 may generate a reconstructed MR image based on multiple coil imagesusing an SOS algorithm. As another example, image processing device 120may generate a reconstructed MR image based on multiple coil imagesusing a GA algorithm.

Intensity inhomogeneity correction technique may include a homomorphicfiltering algorithm, a homomorphic un-sharp masking (HUM) algorithm, asurface fitting algorithm, a nonparametric non-uniform intensitynormalization (N3) algorithm, an bias field corrector (BFC) algorithm, amaximum-likelihood based algorithm, a fuzzy c-means algorithm, ahistogram matching algorithm, or the like, or a combination thereof.

Terminal 130 may be connected to or communicate with image processingdevice 120. Terminal 130 may allow one or more operators (e.g., adoctor, an imaging technician, etc.) to control the production and/ordisplay of images on display 140. Terminal 130 may include an inputdevice, an output device, a control panel (not shown in FIG. 1), or thelike, or a combination thereof. The input device may be a keyboard, atouch screen, a mouse, a remote controller, a wearable device, or thelike, or a combination thereof. An input device may include alphanumericand other keys that may be inputted via a keyboard, a touch screen(e.g., with haptics or tactile feedback, etc.), a speech input, an eyetracking input, a brain monitoring system, or any other comparable inputmechanism. The input information received through the input device maybe communicated to image processing device 120 via network 160 forfurther processing. Another type of the input device may include acursor control device, such as a mouse, a trackball, or cursor directionkeys to communicate direction information and command selections to, forexample, image processing device 120 and to control cursor movement ondisplay 140 or another display device.

Display 140 may display information. The information may include animage before and/or after image processing, a request for input orparameter relating to image acquisition and/or processing, or the like,or a combination thereof. Display 140 may include a liquid crystaldisplay (LCD), a light emitting diode (LED)-based display, a flat paneldisplay or curved screen (or television), a cathode ray tube (CRT), orthe like, or a combination thereof.

Database 150 may store images and/or relevant information or parameters.The images may include an MR image (e.g., coil image, reconstructed MRimage, etc.), a processed MR image (e.g., segmented MR image, correctedMR image, etc.). The parameters may include the magnetic fieldintensity, the resolution of an MR image, the TE of RF pulses, the TR ofRF pulses, and the bandwidth of an MR image, the field-of-view (FOV) ofan MR image, or the like, or a combination thereof.

Network 160 may establish connection between different units in imagingsystem 100. Network 160 may be a single network, or a combination ofvarious networks. Network 160 may be a wired network or a wirelessnetwork. The wired network may include using a Local Area Network (LAN),a Wide Area Network (WAN), a Bluetooth, a ZigBee, a Near FieldCommunication (NFC), or the like, or a combination thereof. The wirelessnetwork may be a Bluetooth, a Near Field Communication (NFC), a wirelesslocal area network (WLAN), WiFi, a Wireless Wide Area Network (WWAN), orthe like, or a combination thereof.

It should be noted that the descriptions above in relation to imagingsystem 100 is provided for the purposes of illustration, and notintended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, various variations and modificationsmay be conducted under the guidance of the present disclosure. However,those variations and modifications do not depart the scope of thepresent disclosure. For example, part or all of the image generated byimaging processing device 120 may be processed by terminal 130. In someembodiments, terminal 130 and display 140 may be combined with or partof image processing device 120 as a single device. Similar modificationsshould fall within the scope of the present disclosure.

FIG. 2 illustrates an architecture of a computer 200 on which aspecialized system incorporating the present teaching may beimplemented. Such a specialized system incorporating the presentteaching has a functional block diagram illustration of a hardwareplatform that may include user interface elements. Computer 200 may be ageneral purpose computer or a special purpose computer. Computer 200 maybe used to implement any component of image processing as describedherein. For example, image processing device 120 may be implemented on acomputer such as computer 200, via its hardware, software program,firmware, or a combination thereof. Although only one such computer isshown, for convenience, the computer functions relating to imageprocessing as described herein may be implemented in a distributedfashion on a number of similar platforms, to distribute the processingload. In some embodiments, computer 200 may be used as imagingprocessing device 120 shown in FIG. 1.

Computer 200, for example, may include communication (COM) ports 211connected to and from a network connected thereto to facilitate datacommunications. Computer 200 may also include a central processing unit(CPU) 205, in the form of one or more processors, for executing programinstructions. The exemplary computer platform may include an internalcommunication bus 204, program storage, and data storage of differentforms, e.g., disk 208, read only memory (ROM) 206, or random accessmemory (RAM) 207, for various data files to be processed and/orcommunicated by the computer, as well as possibly program instructionsto be executed by CPU 205. Computer 200 may also include an I/Ocomponent 209, supporting input/output flows between the computer andother components therein such as user interface elements 213. Computer200 may also receive programming and data via network communications.

Aspects of the methods of the image processing and/or other processes,as described herein, may be embodied in programming. Program aspects ofthe technology may be thought of as “products” or “articles ofmanufacture” typically in the form of executable code and/or associateddata that is carried on or embodied in a type of machine readablemedium. Tangible non-transitory “storage” type media include any or allof the memory or other storage for the computers, processors, or thelike, or associated modules thereof, such as various semiconductormemories, tape drives, disk drives and the like, which may providestorage at any time for the software programming.

All or portions of the software may at times be communicated through anetwork such as the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, froma management server or host computer of a scheduling system into thehardware platform(s) of a computing environment or other systemimplementing a computing environment or similar functionalities inconnection with image processing. Thus, another type of media that maybear the software elements includes optical, electrical andelectromagnetic waves, such as used across physical interfaces betweenlocal devices, through wired and optical landline networks and overvarious air-links. The physical elements that carry such waves, such aswired or wireless links, optical links or the like, also may beconsidered as media bearing the software. As used herein, unlessrestricted to tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

A machine-readable medium may take many forms, including but not limitedto, a tangible storage medium, a carrier wave medium or physicaltransmission medium. Non-volatile storage media include, for example,optical or magnetic disks, such as any of the storage devices in anycomputer(s), or the like, which may be used to implement the system orany of its components shown in the drawings. Volatile storage media mayinclude dynamic memory, such as a main memory of such a computerplatform. Tangible transmission media may include coaxial cables; copperwire and fiber optics, including the wires that form a bus within acomputer system. Carrier-wave transmission media may take the form ofelectric or electromagnetic signals, or acoustic or light waves such asthose generated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media may include, forexample: a floppy disk, a flexible disk, hard disk, magnetic tape, anyother magnetic medium, a CD-ROM, DVD or DVD-ROM, any other opticalmedium, punch cards paper tape, any other physical storage medium withpatterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM, any othermemory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer may read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to a physicalprocessor for execution.

Those skilled in the art will recognize that the present teachings areamenable to a variety of modifications and/or enhancements. For example,although the implementation of various components described herein maybe embodied in a hardware device, it may also be implemented as asoftware only solution—e.g., an installation on an existing server. Inaddition, image processing as disclosed herein may be implemented as afirmware, firmware/software combination, firmware/hardware combination,or a hardware/firmware/software combination.

FIG. 3A illustrates an exemplary image processing device 120 accordingto some embodiments of the present disclosure. Image processing device120 may include a coil image generation module 310, a reconstructionmodule 330, and a correction module 350. Components in image processingdevice 120 may be connected to or communicate with each other and/orother components in imaging system 100, for example, MRI device 110,terminal 130, display 140, database 150, or the like, or a combinationthereof.

Coil image generation module 310 may generate a coil image. As usedherein, a coil image may be a reconstructed MR image generated based onMR image data collected by a coil. The MR image data may be obtained byMRI device 110 or retrieved from another source (e.g., database 150, astorage, etc.). The coil image may be sent to one or more othercomponents in image processing device 120, for example, reconstructionmodule 330, correction module 350, or the like, or a combinationthereof. The coil image may be sent to one or more components in imagingsystem 100, for example, terminal 130, display 140, database 150, or thelike, or a combination thereof.

In some embodiments, the MR image data may be obtained by MRI device110. Considering that different parts of an RF receiver coil may havedifferent sensitivity with respect to MR signals, the coil imagegenerated based on MR image data collected by MRI device 110 may haveintensity inhomogeneity.

In some embodiments, image processing device 120 may generate multiplecoil images based on MR image data obtained by MRI device 110 withmultiple RF receiver coils. In that case, image processing device 120may generate a coil image corresponding to each RF receiver coil. Themultiple coil images may have different intensity inhomogeneity due tothe differences in the sensitivity of various RF receiver coils in MRIdevice 110.

Reconstruction module 330 may perform image reconstruction to generate areconstructed image. The reconstructed image may be a 4D reconstructedMR image, a 3D reconstructed MR image, or a 2D reconstructed MR image.The reconstructed image may be a grayscale image, an RGB image, or abinary image. The reconstructed image generated by reconstruction module330 may be sent to other component(s) in image processing device 120,for example, correction module 350, or the like, or a combinationthereof. The reconstructed image may be sent to one or more componentsin imaging system 100, for example, terminal 130, display 140, database150, or the like, or a combination thereof.

Reconstruction module 330 may generate a reconstructed image based on MRimage data or multiple coil images. The MR image data may be obtained byMRI device 110 or retrieved from another source (e.g., database 150, astorage, etc.). The multiple coil images may be generated by coil imagegeneration module 310 or retrieved from another source (e.g., database150, a storage, etc.).

Reconstruction module 330 may generate multiple reconstructed imagesbased on multiple coil images. The multiple reconstructed images may begenerated based on a same coil image set or different coil image sets.As used herein, a coil image set may include one or more coil images.The multiple reconstructed images may be generated using a samealgorithm or different algorithms. A first reconstructed image may begenerated based on a first set of multiple coil images. A secondreconstructed image may be generated based on a second set of multiplecoil images. The first set of multiple coil images and the second set ofmultiple coil images may be the same or different. For example, thefirst set of multiple coil images and the second set of multiple coilimages may be generated based on MR image data obtained by the same MRIdevice 110 at the same time or at different times. As another example,the first set of multiple coil images and the second set of multiplecoil images may be generated based on MR image data obtained bydifferent MRI devices 110 at the same time or at different times.

Reconstruction module 330 may perform image reconstruction based on animage reconstruction technique. The image reconstruction technique basedon MR image data may include a SMASH algorithm, an AUTO-SMASH algorithm,a variable density AUTO-SMASH algorithm, a GRAPPA algorithm, ageneralized-SMASH algorithm, a SPACE RIP algorithm, or the like, or acombination thereof. The image reconstruction technique based onmultiple coil images may include an SOS algorithm, a GA algorithm, aSENSE algorithm, a PILS algorithm, an MSENSE algorithm, and a SPACE RIPalgorithm, or the like, or a combination thereof.

In some embodiments, image processing device 120 may generate tworeconstructed MR images based on multiple coil images. For example, afirst reconstructed MR image may be reconstructed using an SOS algorithmbased on a first set of coil images and a second reconstructed MR imagemay be reconstructed using a GA algorithm based on a second set of coilimages. The first set of coil images and the second set of coil imagemay be the same or different.

In some embodiments, reconstruction module 330 may include a sum squaresreconstruction unit 331 (shown in FIG. 3B), a geometric averagereconstruction unit 333 (shown in FIG. 3B), and a storage unit (notshown in FIG. 3A). Sum of squares reconstruction unit 331 may generate areconstructed image based on multiple coil images using an SOSalgorithm. Geometric average reconstruction unit 333 may generate areconstructed image based on multiple coil images using a GA algorithm.The storage unit may be used to storage MR image data, a reconstructedMR image, or the like, or a combine thereof.

Correction module 350 may correct an image. The image may be a coilimage, a reconstructed image, or the like, or a combination thereof. Theimage may be generated by coil image generation module 310,reconstruction module 330 or retrieved from another source (e.g.,database 150, and a storage, etc.). Correction module 350 may correctintensity inhomogeneity, distortion, gradient nonlinearity, motionartifact, or the like, or a combination thereof.

In some embodiments, correction module 350 may correct intensityinhomogeneity of an image. Intensity inhomogeneity may also be referredto as intensity nonuniformity. The brightness of an image with intensityinhomogeneity may be distributed nonuniformly. Correction module 350 maycorrect intensity inhomogeneity of an image based on an intensityinhomogeneity correction algorithm. The intensity inhomogeneitycorrection algorithm may include a homomorphic filtering algorithm, anHUM algorithm, a surface fitting algorithm, an N3 algorithm, a BFCalgorithm, a maximum-likelihood based algorithm, a fuzzy c-meansalgorithm, a histogram matching algorithm, or the like, or a combinationthereof.

In some embodiments, correction module 350 may correct intensityinhomogeneity based on two reconstructed images. The two reconstructedimage may be reconstructed based on a same MR data set or a same set ofmultiple coil images. More descriptions about correcting intensityinhomogeneity based on two reconstructed images may be found elsewherein the present disclosure. See, for example, FIG. 7 and FIG. 9 and thedescriptions thereof.

In some embodiments, correction module 350 may include a reconstructedimage obtainment unit 351 (shown in FIG. 3C), a correction informationgeneration unit 353 (shown in FIG. 3C), a correction informationapplication unit 355 (shown in FIG. 3C), and a storage unit (not shownin FIG. 3C). Reconstructed image obtainment unit 351 may obtain areconstructed image. Correction information generation unit 353 maygenerate correction information of a reconstructed image. Correctioninformation application unit 355 may correct a reconstructed image basedon correction information. The storage unit may be used to storage MRimage data, reconstructed MR image, correction information, or the like,or a combine thereof.

FIG. 3B illustrates an exemplary reconstruction module 330 according tosome embodiments of the present disclosure. Reconstruction module 330may include a sum of squares reconstruction unit 331 and a geometricaverage reconstruction unit 333.

Sum of squares reconstruction unit 331 may generate a reconstructedimage based on multiple coil images using an SOS algorithm. Geometricaverage reconstruction unit 333 may generate a reconstructed image basedon multiple coil images using a GA algorithm. More descriptions aboutthe SOS algorithm and the GA algorithm may be found elsewhere in thepresent disclosure. See, for example, FIG. 5 and FIG. 9 and thedescriptions thereof.

FIG. 3C illustrates an exemplary correction module 350 according to someembodiments of the present disclosure. Correction module 350 may includea reconstructed image obtainment unit 351, a correction informationgeneration unit 353, and a correction information application unit 355.

Reconstructed image obtainment unit 351 may obtain a reconstructedimage. The reconstructed image may be a 4D reconstructed image, a 3Dreconstructed image, or a 2D reconstructed image. The reconstructedimage may be obtained from reconstruction module 330 or retrieved fromanother source (e.g., a database 150, a storage, etc.). In someembodiments, reconstructed image obtainment unit 351 may obtain multiplereconstructed images. The multiple reconstructed images may be generatedbased on the same MR data set or different MR data sets. The multiplereconstructed images may be generated based on a same set of multiplecoil images or different sets of multiple coil images. The multiplereconstructed images may be generated using a same reconstructiontechnique or different reconstruction techniques.

Correction information generation unit 353 may generate correctioninformation of a reconstructed image. The correction information mayinclude correction information related to intensity inhomogeneity,distortion information, gradient nonlinearity information, motionartifact information, or the like, or a combination thereof. Thecorrection information may be generated by various ways. For example,the correction information may be generated by smoothing thereconstructed image to capture or emphasize information of interestcontained in the reconstructed image. As another example, the correctioninformation may be generated by comparing the reconstructed image withanother reconstructed image. The two reconstruction images may begenerated based on the same MR image data or a same set of coil images.As a further example, the correction information may be generated basedon spatial sensitivity of the multiple RF receiver coils. Moredescriptions about the correction information generation may be foundelsewhere in the present disclosure. See, for example, FIG. 6 and thedescription thereof. In some embodiments, the correction information maybe correction information related to intensity inhomogeneity. Thecorrection information related to intensity inhomogeneity may be used tocorrect intensity inhomogeneity.

Correction information application unit 355 may correct a reconstructedimage based on correction information. The correction information may begenerated by correction information generation unit or retrieved fromanother source (e.g., database 150, a storage, etc.). More descriptionsabout the correcting reconstructed image based on correction informationmay be found elsewhere in the present disclosure. See, for example, FIG.6 and the description thereof.

It should be noted that the descriptions above in relation to imageprocessing device 120 is provided for the purposes of illustration, andnot intended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, various variations and modificationsmay be conducted under the guidance of the present disclosure. However,those variations and modifications do not depart the scope of thepresent disclosure. For example, reconstruction module 330 may includeone or more other reconstruction unit (not shown in figures) utilizingother reconstruction technique (e.g., a SENSE algorithm, a GA algorithm,a PILS algorithm, an MSENSE algorithm, SPACE RIP, etc.). As anotherexample, correction information generation unit 350 may include adivided image generation subunit, an image smoothing subunit, and/or animage normalization subunit (not shown in figures.).

FIG. 4 illustrates a flowchart illustrating an exemplary process 400 forimage reconstruction in accordance with some embodiments of the presentdisclosure. In some embodiments, process 400 may be performed by one ormore devices (e.g., image processing device 120) in imaging system 100(shown in FIG. 1) and image processing device 120 (shown in FIG. 3A). Insome embodiments, at least part of process 400 may be performed bycomputer 200 shown in FIG. 2.

In 410, multiple coil images may be obtained. The multiple coil imagesmay be generated by coil image generation module 310 or retrieved fromanother source (e.g., database 150, a storage, etc.). Detaileddescriptions about coil images may be found elsewhere in the presentdisclosure. See, for example, FIG. 1 and FIG. 3 and the descriptionthereof. In some embodiments, the multiple coil images may be generatedby coil image generation module 310 based on MR image data collected byMill device 110 of imaging system 100.

In 420, a reconstructed image may be generated based on multiple coilimages. Image reconstruction in 420 may be performed by reconstructionmodule 330 illustrated in FIG. 3A. The reconstructed image may begenerated based on multiple coil images utilizing an imagereconstruction technique including an SOS algorithm, a GA algorithm, aSENSE algorithm, a PILS algorithm, and an MSENSE algorithm, a SPACE RIPalgorithm, or the like, or a combination thereof. More than onereconstructed images may be generated in 420. The more than onereconstructed images may be generated based on a same coil image set ordifferent coil image sets. The more than one reconstructed images may begenerated using a same algorithm or different algorithms. Merely by wayof example, the reconstructed image may be generated based on multiplecoil images utilizing an SOS algorithm and/or a GA algorithm. Moredescriptions about the SOS algorithm and the GA algorithm may be foundelsewhere in the present disclosure. See, for example, FIG. 5 and FIG. 9and the descriptions thereof.

In 430, a reconstructed image may be corrected to generate a correctedimage. The image correction in 430 may be performed by correction module350 illustrated in FIG. 3A. The correction operation in 430 may includeintensity inhomogeneity correction, distortion correction, gradientnonlinearity correction, motion artifact correction, or the like, or acombination thereof.

In some embodiments, the intensity inhomogeneity of a reconstructedimage may be corrected using an intensity inhomogeneity correctionalgorithm. The intensity inhomogeneity correction algorithm may includea homomorphic filtering algorithm, an HUM algorithm, a surface fittingalgorithm, an N3 algorithm, a BFC algorithm, a maximum-likelihood basedalgorithm, a fuzzy c-means algorithm, a histogram matching algorithm, orthe like, or a combination thereof.

In some embodiments, correction module 350 may correct intensityinhomogeneity based on two reconstructed images. The two reconstructedimages may be obtained based on a same MR data set or a same set ofmultiple coil images. More descriptions about correcting intensityinhomogeneity based on two reconstructed images may be found elsewherein the present disclosure. See, for example, FIG. 7 and FIG. 9 and thedescriptions thereof.

It should be noted that process 400 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protection scope of the presentdisclosure. In some embodiments, some steps may be reduced or added. Forexample, 430 may be changed and a noise reduction algorithm may be usedto process the reconstructed image. As another example, 420 may beomitted and intensity inhomogeneity of coil images may be corrected in430.

FIG. 5 is a flowchart illustrating an exemplary process 500 for imagereconstruction in accordance with some embodiments of the presentdisclosure. In some embodiments, process 500 may be performed byreconstruction module 330 in imaging processing device 120. In someembodiments, process 500 described with reference to FIG. 5 may be anexemplary process for achieving 420 shown in FIG. 4.

In 510, pixel coordinates of corresponding pixels in multiple coilimages may be determined. The corresponding pixels in the multiple coilimages may relate to a point of the imaged object. The multiple coilimages may be reconstructed images of a same imaged object.

In 520, pixel values of corresponding pixels in the multiple coil imagesmay be obtained. As used herein, a pixel value may refer to the greyvalue of a pixel. Suppose that there are n coil images, as forcorresponding pixels of a point of the imaged object, the pixel valuesof the corresponding pixels may be denoted as P₁, P₂, P₃, . . . , P_(n).

In 530, a reconstructed image may be generated based on pixelcoordinates and pixel values of corresponding pixels in the multiplecoil images. The pixel value of a point in the imaged object inreconstructed image may be denoted as P. The pixel value P may bedetermined based on the pixel values of corresponding pixels P₁, P₂, P₃,. . . , P_(n). For example, the pixel value P may be a statisticalparameter (e.g., an average value, a median value, a mode, a sum ofsquares, a geometric mean, etc.) of the pixel values of correspondingpixels P₁, P₂, P₃, . . . , P_(n).

In some embodiments, pixel value P may be determined based on the pixelvalues of corresponding pixels using an SOS algorithm. In that case,process 500 may be performed by sum of squares reconstruction unit 331in imaging processing device 120. The SOS algorithm may be performedaccording to Equation (1) below:

$\begin{matrix}{{P = \sqrt[2]{\sum\limits_{i = 1}^{n}P_{i}^{2}}},} & (1)\end{matrix}$

where n refers to a number of the coil images.

In some embodiments, pixel value P may be determined based on the pixelvalues of corresponding pixels using a GA algorithm. In that case,process 500 may be performed by geometric average reconstruction unit333 in imaging processing device 120. The GA algorithm may be performedaccording to Equation (2) below:

$\begin{matrix}{{P = \sqrt[n]{P_{1}*P_{2}*P_{3}*\; \ldots \mspace{11mu}*P_{n}}},} & (2)\end{matrix}$

where n refers to a number of the coil images.

It should be noted that process 500 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protecting scope of the presentdisclosure. In some embodiments, in 510, multiple pixel coordinates maybe determined and the pixel values of these multiple pixels inreconstructed image may be determined at the same time. In someembodiments, in 510, a pixel coordinates may be determined. In thatcase, pixel values of pixels in reconstructed image may be determinedsuccessively. Similar modifications should fall within the scope of thepresent disclosure.

FIG. 6 is a flowchart illustrating an exemplary process 600 forcorrecting a reconstructed image in accordance with some embodiments ofthe present disclosure. In some embodiments, process 600 may beperformed by correction module 350 in imaging processing device 120. Insome embodiments, process 600 described with reference to FIG. 6 may bean exemplary process for achieving 430 shown in FIG. 4.

In 610, a reconstructed image may be obtained. The obtainment of thereconstructed image may be performed by reconstructed image obtainmentunit 351. The reconstructed image may be a 4D reconstructed image, a 3Dreconstructed image, or a 2D reconstructed image. The reconstructedimage may be obtained from reconstruction module 330 or retrieved fromanother source (e.g., a database 150, a storage, etc.). One or morereconstructed images may be obtained in 610. The obtained more than onereconstructed images may be generated based on the same MR image data orcoils images. The obtained more than one reconstructed images may begenerated using a same reconstruction technique or differentreconstruction techniques. More descriptions about image reconstructionbe found elsewhere in the present disclosure. See, for example, FIG. 1and FIG. 4 and the descriptions thereof.

In some embodiments, a first reconstructed image and a secondreconstructed image may be obtained in 610. Merely by way of example,the first reconstructed image may be generated using an SOS algorithm,and the second reconstructed image may be generated using a GAalgorithm. Both of the first reconstructed image and the secondreconstructed image may be generated based on MR image data obtained bythe same MRI device 110 from the same scan. In some embodiments, thereconstructed image may be transformed by a transformation algorithm(e.g., a log transformation algorithm, a polar transformation algorithm,etc.).

In 630, correction information about the reconstructed image may begenerated. The correction information may be an approximate correctioninformation. The generation of the correction information may beperformed by correction information generation unit 353. The correctioninformation may include correction information related to intensityinhomogeneity, image distortion information, gradient nonlinearityinformation, motion artifact information, color information, or thelike, or a combination thereof.

The correction information may be generated based on one or morereconstructed images obtained in 610. The correction information may begenerated by comparing multiple reconstructed image. For example, thecorrection information may be generated by dividing the multiplereconstructed images, or by subtracting the multiple reconstructedimages from one other, or the like, or a combination thereof. Thecorrection information may be generated based on one reconstructedimage. For example, the correction information may be generated bysmoothing the reconstructed image to capture or emphasize information ofinterest contained in the reconstructed image. As another example, ifthe reconstructed image is obtained from reconstruction module 330 basedon multiple coil images in 610, the correction information may begenerated based on spatial sensitivity of the multiple RF receivercoils.

In some embodiments, the correction information may be correctioninformation related to intensity inhomogeneity. The correctioninformation related to intensity inhomogeneity may be generated based onone or more reconstructed images. In the case of generating correctioninformation related to intensity inhomogeneity based on onereconstructed image, the correction information related to intensityinhomogeneity may be generated by smoothing the reconstructed image.Smoothing an image may tend to capture or emphasize information ofinterest in the image while leaving out noise in the image. Therefore,the smoothed reconstructed image may be used to correct intensityinhomogeneity of a reconstructed image.

The reconstructed image may be smoothed using a smoothing algorithm. Thesmooth algorithm may include a low-pass filter algorithm, an additivesmoothing algorithm, a digital filter algorithm, an exponentialsmoothing algorithm, a Kalman filter, a Kernel smoother algorithm, aKolmogorov-Zurbenko filter algorithm, a Laplacian smoothing algorithm, aRamer-Douglas-Peucker algorithm, a Savitzky-Golay smoothing filteralgorithm, or the like, or a combination thereof. In some embodiment, areconstructed image may be smoothed by low-pass filter. The low passfilter may be a Gaussian filter, a Butterworth filter, a Chebyshevfilter, or the like, or a combination thereof.

In the case of generating correction information related to intensityinhomogeneity based on multiple reconstructed images, the correctioninformation related to intensity inhomogeneity may be generated bydividing the multiple reconstructed image. For example, the correctioninformation related to intensity inhomogeneity may be generated bydividing a first reconstructed image by a second reconstructed image.The first reconstructed image and the second reconstructed image may begenerated based on a same MR image data set or a same set of multiplecoil images. The divided reconstructed image of the first reconstructedimage and the second reconstructed may contain correction informationrelated to intensity inhomogeneity used to correct intensityinhomogeneity of the first reconstructed image and/or secondreconstructed image. More descriptions about obtaining correctioninformation related to intensity inhomogeneity on two reconstructedimages may be found elsewhere in the present disclosure. See, forexample, FIG. 7 and FIG. 9 and the descriptions thereof.

In 650, a corrected reconstructed image may be generated based on thecorrection information. The operation 650 may be performed by correctioninformation application unit 355. In some embodiments, the correctioninformation may be correction information related to intensityinhomogeneity. In 650, intensity inhomogeneity of reconstructed imagemay be corrected to generate a corrected reconstructed image.

Merely by way of example, a smoothed reconstructed image or a dividedreconstructed image that may be used to correct intensity inhomogeneitymay be generated in 630. The corrected reconstructed image may begenerated by dividing the reconstructed image by the smoothedreconstructed image or the divided reconstructed image. Dividing thereconstructed image by the smoothed reconstructed image means dividingthe pixel values of all pixels in the reconstructed image by the pixelvalues of corresponding pixels in the smoothed reconstructed image. Acorresponding pixel in the smoothed reconstructed image may be a pixelhaving the same coordinates as the pixel in reconstructed image.Dividing the reconstructed image by the divided reconstructed meansdividing the pixel values of all pixels in the reconstructed image bythe pixel values of corresponding pixels in the divided reconstructedimage. A corresponding pixel in the divided reconstructed image may be apixel having the same coordinates as the pixel in reconstructed image.

It should be noted that process 600 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protecting scope of the presentdisclosure. For example, in 650, the corrected reconstructed image maybe generated by multiplying the reconstructed image by the reciprocal ofthe smoothed reconstructed image. Multiplying the reconstructed image bythe reciprocal of the smoothed reconstructed image means multiplying thepixel values of all pixels in the reconstructed image by the reciprocalsof the pixel values of corresponding pixels in the smoothedreconstructed image. The corresponding pixel in the smoothedreconstructed image may be a pixel having the same coordinates with ofthe pixel in reconstructed image. Similar modifications should fallwithin the scope of the present disclosure.

FIG. 7 is a flowchart illustrating an exemplary process 700 forobtaining correction information in accordance with some embodiments ofthe present disclosure. In some embodiments, process 700 may beperformed by correction module 350 in imaging processing device 120. Insome embodiments, process 700 described with reference to FIG. 7 may bean exemplary process for achieving 630 shown in FIG. 6.

In 710, a first reconstructed image based on a first reconstructionalgorithm may be obtained. In 730, a second reconstructed image based ona second reconstruction algorithm may be obtained. The obtainment of thefirst reconstructed image and the second reconstructed image may beperformed by reconstructed image obtainment unit 351. The reconstructedimage may be a 3D MR reconstructed image or a 2D MR reconstructed image.The first reconstructed image and the second reconstructed image may begenerated based on a same MR data set or a same set of coil images. Thefirst reconstructed image and the second reconstructed image may begenerated based on different reconstruction algorithms (e.g., an SOSalgorithm, a GA algorithm, an iterative algorithm, etc.). Moredescriptions about image reconstruction may be found elsewhere in thepresent disclosure. See, for example, FIG. 1 and FIG. 4 and thedescriptions thereof.

In some embodiments, the first reconstruction image may be generatedbased on an SOS algorithm and the second reconstruction image may begenerated by a GA algorithm. More descriptions about the SOS algorithmand the GA algorithm may be found elsewhere in the present disclosure.See, for example, FIG. 5 and FIG. 9 and the descriptions thereof.

In 750, correction information of the first reconstructed image may begenerated based on the first reconstructed image and the secondreconstructed image. The correction information may include correctioninformation related to intensity inhomogeneity, image distortioninformation, gradient nonlinearity information, motion artifactinformation, color information, or the like, or a combination thereof.

In some embodiments, the correction information may include correctioninformation related to intensity inhomogeneity. The correctioninformation related to intensity inhomogeneity may be generated based onthe difference between the first reconstructed image and the secondreconstructed image. Merely by way of example, the correctioninformation related to intensity inhomogeneity may be determined bydividing the first reconstructed image by the second reconstructedimage. Dividing the first reconstructed image by the secondreconstructed image means dividing the pixel values of all pixels in thefirst reconstructed image by the pixel values of corresponding pixels inthe second reconstructed image. A corresponding pixel in the secondreconstructed image may be a pixel having the same coordinates as thepixel in the first reconstructed image. As another example, thecorrection information related to intensity inhomogeneity may bedetermined by dividing the second reconstructed image by the firstreconstructed image.

Because the first reconstructed image and the second reconstructed imagemay be generated based on a same MR data set or a same set of coilimages, they may contain the same structure information about an imagedobject. Dividing the first reconstructed image by the secondreconstructed image may provide the correction information of the firstreconstructed image (e.g., the intensity inhomogeneity of the firstreconstructed image.). More descriptions about generating correctioninformation may be found elsewhere in the present disclosure. See, forexample, FIG. 8 and the description thereof.

It should be noted that process 700 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protecting scope of the presentdisclosure. For example, 710 and 730 may be performed at the same time.As another example, 730 may be performed before 710. Similarmodifications should fall within the scope of the present disclosure.

FIG. 8 is a flowchart illustrating an exemplary process 800 forgenerating correction information in accordance with some embodiments ofthe present disclosure. In some embodiments, process 800 may beperformed by correction module 350 in imaging processing device 120. Insome embodiments, process 800 described with reference to FIG. 8 may bean exemplary process for achieving 750 shown in FIG. 7.

In 810, a divided image may be generated based on a first reconstructedimage and a second reconstructed image. A divided image may be generatedby dividing the first reconstructed image by the second reconstructedimage. The first reconstructed image and the second reconstructed imagemay be generated based on a same MR data set or a same coil images set.Dividing the first reconstructed image by the second reconstructed imagemeans dividing the pixel values of all pixels in the first reconstructedimage by the pixel values of corresponding pixels in the secondreconstructed image. A corresponding pixel in the second reconstructedimage may be a pixel having the same coordinates as the pixel in thefirst reconstructed image. More descriptions about divided image may befound elsewhere in the present disclosure. See, for example, FIG. 7 andthe descriptions thereof.

In some embodiments, the first reconstructed image may be areconstructed image generated based on an SOS algorithm and the secondreconstructed image may be reconstructed generated based on a GAalgorithm. The first reconstructed image and the second reconstructedmay be generated based on a same MR data set or a same coil images set.In 810, the divided image may be generated by dividing the firstreconstructed image based on the SOS algorithm by the secondreconstructed image based on the GA algorithm.

In 830, the divided image may be smoothed to generate a smoothed dividedimage. Smoothing an image may tend to capture or emphasize informationof interest in the image while leaving out noise, other fine-scalestructure, or transient phenomena in the image. The divided image may besmoothed using a smoothing algorithm. In some embodiments, the dividedimage may be smoothed using a low-pass filter algorithm (e.g., aGaussian filter, a Butterworth filter, a Chebyshev filter, etc.). Moredescriptions about smoothing image may be found elsewhere in the presentdisclosure. See, for example, FIG. 6 and the description thereof.

In 850, the smoothed divided image may be normalized. The operation ofnormalization may be used to make a correction reconstructed image and areconstructed image have a same dynamic range. For example, the overallpixel values of the correction reconstructed image and the reconstructedimage may be the same. As another example, the smoothed divided imagemay be normalized by adjusting the overall pixel values so that itsaverage pixel value may equal to 1.

It should be noted that process 800 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protecting scope of the presentdisclosure. For example, 830 may be omitted. As another example, 850 maybe omitted. As another example, 810 may be performed by multiplying thefirst reconstructed image by the count backwards of the secondreconstructed image. As another example, 850 may be performed before830. The divided image may be normalized first and be smoothed then.Similar modifications should fall within the scope of the presentdisclosure.

FIG. 9 is a flowchart illustrating an exemplary process 900 for imagereconstruction in accordance with some embodiments of the presentdisclosure. In some embodiments, process 900 may be performed by one ormore devices (e.g., image processing device 120) in imaging system 100(shown in FIG. 1) and image processing device 120 (shown in FIG. 3A). Insome embodiments, at least part of process 900 may be performed bycomputer 200 shown in FIG. 2. In some embodiments, process 900 describedwith reference to FIG. 9 may be an exemplary embodiment of process 400shown in FIG. 4.

In 910, a first reconstructed image based on an SOS algorithm may begenerated. In 920, a second reconstructed image based on a GA algorithmmay be generated. The first reconstructed image and/or the secondreconstructed image may be a 3D MR reconstructed image or a 2D MRreconstructed image. The first reconstructed image and the secondreconstructed image may be generated based on a same set of coil imagesby reconstruction module 330 of image processing device 120. Merely byway of example, the first reconstructed image may be generated by an SOSreconstruction unit 331 and the second reconstructed image may begenerated by a GA reconstruction unit 333. More descriptions about theSOS algorithm and the GA algorithm may be found elsewhere in the presentdisclosure. See, for example, FIG. 5 and the descriptions thereof.

The first reconstructed image and the second reconstructed image mayhave intensity inhomogeneity due to the spatial sensitivity of coils.The SOS algorithm may be sensitive to spatial sensitivity of coils butnot to noise. The GA algorithm may be sensitive to noise but not tospatial sensitive of the coils. Therefore, the first reconstructed imagemay tend to have a higher signal-to-noise ratio than the secondreconstructed image. The first reconstructed image may tend to havehigher intensity inhomogeneity than the second reconstructed image. Thesecond reconstructed image may be used to correct the firstreconstructed image.

FIG. 10 illustrates a reconstructed image based on a GA algorithm. FIG.11 illustrates a reconstructed image based on an SOS algorithm. Asillustrated in FIG. 10 and FIG. 11, there are intensity inhomogeneity inthe two reconstructed image. The intensity inhomogeneity in FIG. 11 ishigher than that in FIG. 10.

In 930, a divided image may be generated based on the firstreconstructed image and the second reconstructed image. The dividedimage may be generated by dividing the first reconstructed image by thesecond reconstructed image. Dividing the first reconstructed image bythe second reconstructed image means dividing the pixel values of allpixels in the first reconstructed image by the pixel values ofcorresponding pixels in the second reconstructed image. A correspondingpixel in the second reconstructed image may be a pixel having the samecoordinates with of the pixel in the first reconstructed image. Thedivided image may be used for correcting the first reconstructed image.More descriptions about divided image may be found elsewhere in thepresent disclosure. See, for example, FIG. 8 and the descriptionthereof.

In 940, the divided image may be smoothed to generate a smoothed dividedimage. The divided image may be smoothed to remove or reduce noise whileother information (e.g., correction information related to intensityinhomogeneity, etc.) may remain essentially. In some embodiments, thedivided image may be smoothed using a low-pass filter algorithm (e.g., aGaussian filter, a Butterworth filter, a Chebyshev filter, etc.). Moredescriptions about smoothing image may be found elsewhere in the presentdisclosure. See, for example, FIG. 6 and the descriptions thereof.

In 950, the smoothed divided image may be normalized to generate anormalized image. The operation of normalization may be used to make acorrection reconstructed image and a reconstructed image (e.g., areconstructed image based on an SOS algorithm) have a same dynamicrange. For example, the overall pixel values of the correctionreconstructed image and the reconstructed image may be the same. Foranother example, the smoothed divided image may be normalized byadjusting the overall pixel values so that its average pixel value mayequal to 1.

In 960, the first reconstructed image may be corrected based on thenormalized image. For example, the first reconstructed image may becorrected by dividing the first reconstructed image by the normalizedimage. Because the normalized image may contain intensity inhomogeneityof the first reconstructed image, the intensity inhomogeneity of thefirst reconstructed image may be corrected by dividing the firstreconstructed image by the normalized image. Dividing the firstreconstructed image by the normalized image means dividing the pixelvalues of all pixels in the first reconstructed image by the pixelvalues of corresponding pixels in the normalized image. A correspondingpixel in the normalized image may be a pixel having the same coordinateswith of the pixel in the first reconstructed image.

It should be noted that process 900 described above is provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. Apparently for persons having ordinary skills in theart, numerous variations and modifications may be conducted under theteaching of the present disclosure. However, those variations andmodifications do not depart the protecting scope of the presentdisclosure. In some embodiments, some steps may be reduced or added. Forexample, 940 or 950 may be omitted. As another example, 930 may beperformed before 910 or 930 and 910 may be performed at the same time.In some embodiments, 960 may be performed by multiplying the firstreconstructed image by the count backwards of the normalized image.Similar modifications should fall within the scope of the presentdisclosure.

EXAMPLES

The examples are provided for illustration purposes, and not intended tolimit the scope of the present disclosure.

Example 1

FIG. 10 illustrates a reconstructed liver image based a GA algorithm.FIG. 11 illustrates a reconstructed liver image based on an SOSalgorithm. FIG. 10 and FIG. 11 were generated utilizing the system andprocess according to some embodiments of the present disclosure. Themagnetic field intensity of the main magnet was 1.5 T. The slicethickness was 3 millimeters. The flip angle was 10°. The frequency wasfat saturation. The TE of RF pulses was 2.2 milliseconds and the TR ofRF pulses was 4.9 milliseconds. Forty layers of scanning was performed.Eighty coil images were generated. The BW of the reconstructed image was345 Hz/pixel. The FOV of the reconstructed image was 260 millimeters*260millimeters. The resolution of reconstructed image was 256*256.

As illustrated in FIG. 10 and FIG. 11, there are intensity inhomogeneityin the two reconstructed images. For example, the brightness of area1010 is different from the brightness of area 1030. As another example,the brightness of area 1110 is different from the brightness of area1130. The intensity inhomogeneity in FIG. 11 is higher than that in FIG.10. The difference in brightness between area 1010 and area 1030 of FIG.10 is less significant than the difference in brightness between area1110 and area 1130 of FIG. 11.

Example 2

FIG. 12 illustrates a divided image of a reconstructed liver image basedon an SOS algorithm (shown in FIG. 11) and a reconstructed liver imagebased a GA algorithm (shown in FIG. 10.). The divided image in FIG. 12was normalized. The divided image contains intensity inhomogeneity. Theinformation related to intensity inhomogeneity of the divided image maybe used to correct FIG. 11. As illustrated in FIG. 12, the intensityinhomogeneity changes smoothly and there is some noise in the dividedimage. FIG. 12 may be processed by a Gaussian filter to remove the noisein the divided image.

Example 3

FIG. 13 illustrates a corrected image of a reconstructed liver imagebased on an SOS algorithm (shown in FIG. 11). FIG. 13 was generated bydividing FIG. 11 by the filtered image of FIG. 12. As illustrated inFIG. 13, the intensity inhomogeneity in FIG. 11 was corrected.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “engine,” “unit,” “component,” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the operator's computer, partly on the operator's computer,as a stand-alone software package, partly on the operator's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe operator's computer through any type of network, including a localarea network (LAN) or a wide area network (WAN), or the connection maybe made to an external computer (for example, through the Internet usingan Internet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution—e.g., an installation onan existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities of ingredients,properties, and so forth, used to describe and claim certain embodimentsof the application are to be understood as being modified in someinstances by the term “about,” “approximate,” or “substantially.” Forexample, “about,” “approximate,” or “substantially” may indicate ±20%variation of the value it describes, unless otherwise stated.Accordingly, in some embodiments, the numerical parameters set forth inthe written description and attached claims are approximations that mayvary depending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the descriptions, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

We claim:
 1. A method comprising: obtaining multiple coil images of animaged object; reconstructing a first reconstructed image based on themultiple coil images according to a first reconstruction algorithm;reconstructing a second reconstructed image based on the multiple coilimages according to a second reconstruction algorithm; generatingcorrection information about the first reconstructed image based on thefirst reconstructed image and the second reconstructed image; andgenerating a third reconstructed image based on the first reconstructedimage and the correction information about the first reconstructedimage.
 2. The method of claim 1, wherein the first reconstructionalgorithm is a sum of squares algorithm.
 3. The method of claim 1,wherein the second reconstruction algorithm is a geometric averagealgorithm.
 4. The method of claim 1, wherein the reconstructing a firstreconstructed image or the reconstructing a second reconstructed imagefurther includes: for each point of a plurality of points in the imagedobject determining pixel coordinates of corresponding pixels in themultiple coil images relating to the point of the imaged object; andobtaining pixel values of the corresponding pixels in the multiple coilimages of the point; and reconstructing the first reconstructed image orthe second reconstructed image based on the pixel coordinates and thepixel values of the corresponding pixels in the multiple coil images ofthe plurality of points in the imaged object.
 5. The method of claim 1,wherein the correction information relates to intensity inhomogeneity ofthe first reconstructed image.
 6. The method of claim 5, wherein thegenerating correction information relating to intensity inhomogeneity ofthe first reconstructed image includes: dividing the first reconstructedimage by the second reconstructed image to generate a divided image. 7.The method of claim 6, wherein the generating a third reconstructedimage further includes: dividing the first reconstructed image by thedivided image.
 8. The method of claim 6, wherein the generatingcorrection information relating to intensity inhomogeneity furtherincludes: smoothing the divided image to generate a smoothed dividedimage; and normalizing the smoothed divided image to generate anormalized image.
 9. The method of claim 8, wherein the generating athird reconstructed image further includes: dividing the firstreconstructed image by the normalized image.
 10. A system comprising: acoil image generation module configured to generate multiple coil imagesof an imaged object; a reconstruction module configured to generate afirst reconstructed image based on the multiple coil images according toa first reconstruction algorithm and generate a second reconstructedimage based on the multiple coil images according to a secondreconstruction algorithm; a correction module configured to generate athird reconstructed image by correcting the first reconstructed imagebased on the second reconstructed image.
 11. The system of claim 10, thereconstruction module further includes: a sum of squares reconstructionunit configured to generate the first reconstructed image based on themultiple coil images using a sum of squares algorithm; and a geometricaverage reconstruction unit configured to generate the secondreconstructed image based on the multiple coil images using a geometricalgorithm.
 12. The system of claim 10, the correction module furtherincludes: a reconstructed image obtainment unit configured to obtain areconstructed image; a correction information generation unit configuredto generate correction information of the reconstructed image; and acorrection information application unit configured to correct thereconstructed image based on the correction information.
 13. The systemof claim 10, wherein the reconstructing a first reconstructed image orthe reconstructing a second reconstructed image further includes: foreach point of a plurality of points in the imaged object determiningpixel coordinates of corresponding pixels in the multiple coil imagesrelating to the point of the imaged object; obtaining pixel values ofthe corresponding pixels in the multiple coil images of the point; andreconstructing the first reconstructed image or the second reconstructedimage based on the pixel coordinates and the pixel values of thecorresponding pixels in the multiple coil images of the plurality ofpoints in the imaged object.
 14. The system of claim 12, wherein thecorrection information relates to intensity inhomogeneity information ofthe first reconstructed image.
 15. The system of claim 14, wherein thegenerating the correction information relating to the intensityinhomogeneity includes: dividing the first reconstructed image by thesecond reconstructed image to generate a divided image.
 16. The systemof claim 15, wherein the generating a third reconstructed image furtherincludes: dividing the first reconstructed image by the divided image.17. The system of claim 15, wherein the generating correctinginformation relating to intensity inhomogeneity further includes:smoothing the divided image to generate a smoothed divided image; andnormalizing the smoothed divided image to generate a normalized image.18. The method of claim 17, wherein the generating a third reconstructedimage further includes: dividing the first reconstructed image by thenormalized image.
 19. A non-transitory computer readable mediumcomprising executable instructions that, when executed by at least oneprocessor, cause the at least one processor to effectuate a methodcomprising: obtaining multiple coil images; reconstructing a firstreconstructed image based on the multiple coil images based on a firstreconstruction algorithm; reconstructing a second reconstructed imagebased on the multiple coil images based on a second reconstructionalgorithm; generating correction information about the firstreconstructed image based on the first reconstructed image and thesecond reconstructed image; and generating a third reconstructed imagebased on the first reconstructed image and the correction informationabout the first reconstructed image.